I have a Polars dataframe like below:
df_cat = pl.DataFrame(
[
pl.Series("a_cat", ["c", "a", "b", "c", "b"], dtype=pl.Categorical),
pl.Series("b_cat", ["F", "G", "E", "G", "G"], dtype=pl.Categorical)
])
print(df_cat)
shape: (5, 2)
┌───────┬───────┐
│ a_cat ┆ b_cat │
│ --- ┆ --- │
│ cat ┆ cat │
╞═══════╪═══════╡
│ c ┆ F │
├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ a ┆ G │
├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ b ┆ E │
├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ c ┆ G │
├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ b ┆ G │
└───────┴───────┘
The following filter runs perfectly fine:
print(df_cat.filter(pl.col('a_cat') == 'c'))
shape: (2, 2)
┌───────┬───────┐
│ a_cat ┆ b_cat │
│ --- ┆ --- │
│ cat ┆ cat │
╞═══════╪═══════╡
│ c ┆ F │
├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ c ┆ G │
└───────┴───────┘
What I want is to use a list of string to run the filter more efficiently. So I tried and ended up with the following error message:
print(df_cat.filter(pl.col('a_cat').is_in(['a', 'c'])))
---------------------------------------------------------------------------
ComputeError Traceback (most recent call last)
d:\GitRepo\Test2\stockEMD3.ipynb Cell 9 in <cell line: 1>()
----> 1 print(df_cat.filter(pl.col('a_cat').is_in(['c'])))
File c:\ProgramData\Anaconda3\envs\charm3.9\lib\site-packages\polars\internals\dataframe\frame.py:2185, in DataFrame.filter(self, predicate)
2181 if _NUMPY_AVAILABLE and isinstance(predicate, np.ndarray):
2182 predicate = pli.Series(predicate)
2184 return (
-> 2185 self.lazy()
2186 .filter(predicate) # type: ignore[arg-type]
2187 .collect(no_optimization=True, string_cache=False)
2188 )
File c:\ProgramData\Anaconda3\envs\charm3.9\lib\site-packages\polars\internals\lazyframe\frame.py:660, in LazyFrame.collect(self, type_coercion, predicate_pushdown, projection_pushdown, simplify_expression, string_cache, no_optimization, slice_pushdown)
650 projection_pushdown = False
652 ldf = self._ldf.optimization_toggle(
653 type_coercion,
654 predicate_pushdown,
(...)
658 slice_pushdown,
659 )
--> 660 return pli.wrap_df(ldf.collect())
ComputeError: joins/or comparisons on categorical dtypes can only happen if they are created under the same global string cache
From this Stackoverflow link I understand "You need to set a global string cache to compare categoricals created in different columns/lists." but my question is
Why the == one single string filter case works?
What is the proper way to filter a categorical column with a list of string?
Thanks!
Actually, you don't need to set a global string cache to compare strings to Categorical variables. You can use cast to accomplish this.
Let's use this data. I've included the integer values that underlie the Categorical variables to demonstrate something later.
import polars as pl
df_cat = (
pl.DataFrame(
[
pl.Series("a_cat", ["c", "a", "b", "c", "X"], dtype=pl.Categorical),
pl.Series("b_cat", ["F", "G", "E", "S", "X"], dtype=pl.Categorical),
]
)
.with_column(
pl.all().to_physical().suffix('_phys')
)
)
df_cat
shape: (5, 4)
┌───────┬───────┬────────────┬────────────┐
│ a_cat ┆ b_cat ┆ a_cat_phys ┆ b_cat_phys │
│ --- ┆ --- ┆ --- ┆ --- │
│ cat ┆ cat ┆ u32 ┆ u32 │
╞═══════╪═══════╪════════════╪════════════╡
│ c ┆ F ┆ 0 ┆ 0 │
├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ a ┆ G ┆ 1 ┆ 1 │
├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ b ┆ E ┆ 2 ┆ 2 │
├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ c ┆ S ┆ 0 ┆ 3 │
├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ X ┆ X ┆ 3 ┆ 4 │
└───────┴───────┴────────────┴────────────┘
Comparing a categorical variable to a string
If we cast a Categorical variable back to its string values, we can make any comparison we need. For example:
df_cat.filter(pl.col('a_cat').cast(pl.Utf8).is_in(['a', 'c']))
shape: (3, 4)
┌───────┬───────┬────────────┬────────────┐
│ a_cat ┆ b_cat ┆ a_cat_phys ┆ b_cat_phys │
│ --- ┆ --- ┆ --- ┆ --- │
│ cat ┆ cat ┆ u32 ┆ u32 │
╞═══════╪═══════╪════════════╪════════════╡
│ c ┆ F ┆ 0 ┆ 0 │
├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ a ┆ G ┆ 1 ┆ 1 │
├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ c ┆ S ┆ 0 ┆ 3 │
└───────┴───────┴────────────┴────────────┘
Or in a filter step comparing the string values of two Categorical variables that do not share the same string cache.
df_cat.filter(pl.col('a_cat').cast(pl.Utf8) == pl.col('b_cat').cast(pl.Utf8))
shape: (1, 4)
┌───────┬───────┬────────────┬────────────┐
│ a_cat ┆ b_cat ┆ a_cat_phys ┆ b_cat_phys │
│ --- ┆ --- ┆ --- ┆ --- │
│ cat ┆ cat ┆ u32 ┆ u32 │
╞═══════╪═══════╪════════════╪════════════╡
│ X ┆ X ┆ 3 ┆ 4 │
└───────┴───────┴────────────┴────────────┘
Notice that it is the string values being compared (not the integers underlying the two Categorical variables).
The equality operator on Categorical variables
The following statements are equivalent:
df_cat.filter((pl.col('a_cat') == 'a'))
df_cat.filter((pl.col('a_cat').cast(pl.Utf8) == 'a'))
The former is syntactic sugar for the latter, as the former is a common use case.
As the error states: ComputeError: joins/or comparisons on categorical dtypes can only happen if they are created under the same global string cache.
Comparisons of categorical values are only allowed under a global string cache. You really want to set this in such a case as it speeds up comparisons and prevents expensive casts to strings.
Setting this on the start of your query will ensure it runs:
import polars as pl
pl.Config.set_global_string_cache()
This is a new answer based on the one from #ritchie46.
Polar 0.15.15 it now is
import polars as pl
pl.toggle_string_cache(True)
Also a StringCache() Context manager can be used, see polars documentation:
with pl.StringCache():
print(df_cat.filter(pl.col('a_cat').is_in(['a', 'c'])))
Related
I would like to do an ordinal encoding of a column. Pandas has the nice and convenient method of pd.factorize(), however, I would like to achieve the same in polars.
df = pl.DataFrame({"a": [5, 8, 10], "b": ["hi", "hello", "hi"]})
┌─────┬───────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ str │
╞═════╪═══════╡
│ 5 ┆ hi │
├╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 8 ┆ hello │
├╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 10 ┆ hi │
└─────┴───────┘
desired result:
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 0 ┆ 0 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 1 ┆ 1 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 2 ┆ 0 │
└─────┴─────┘
You can join with a dummy DataFrame that contains the unique values and the ordinal encoding you are interested in:
df = pl.DataFrame({"a": [5, 8, 10], "b": ["hi", "hello", "hi"]})
unique = df.select(
pl.col("b").unique(maintain_order=True)
).with_row_count(name="ordinal")
df.join(unique, on="b")
Or you could "misuse" the fact that categorical values are backed by u32 integers.
df.with_column(
pl.col("b").cast(pl.Categorical).to_physical().alias("ordinal")
)
Both methods output:
shape: (3, 3)
┌─────┬───────┬─────────┐
│ a ┆ b ┆ ordinal │
│ --- ┆ --- ┆ --- │
│ i64 ┆ str ┆ u32 │
╞═════╪═══════╪═════════╡
│ 5 ┆ hi ┆ 0 │
├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
│ 8 ┆ hello ┆ 1 │
├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
│ 10 ┆ hi ┆ 0 │
└─────┴───────┴─────────┘
Here's another way to do it although I doubt it's better than the dummy Dataframe from #ritchie46
df.with_columns([pl.col('b').unique().list().alias('uniq'),
pl.col('b').unique().list().arr.eval(pl.element().rank()).alias('uniqid')]).explode(['uniq','uniqid']).filter(pl.col('b')==pl.col('uniq')).select(pl.exclude('uniq')).with_column(pl.col('uniqid')-1)
There's almost certainly a way to improve this but basically it creates a new column called uniq which is a list of all the unique values of the column as well as uniqid which (I think, and seems to be) the 1-index based order of the values. It then explodes those creating a row for ever value in uniq and then filters out the ones rows that don't equal the column b. Since rank gives the 1-index (rather than 0-index) you have to subtract 1 and exclude the uniq column that we don't care about since it's the same as b.
If the order is not important you could use .rank(method="dense")
>>> df.select(pl.all().rank(method="dense") - 1)
shape: (3, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ u32 ┆ u32 │
╞═════╪═════╡
│ 0 ┆ 1 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 1 ┆ 0 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 2 ┆ 1 │
└─────┴─────┘
If it is - you could:
>>> (
... df.with_row_count()
... .with_columns([
... pl.col("row_nr").first()
... .over(col)
... .rank(method="dense")
... .alias(col) - 1
... for col in df.columns
... ])
... .drop("row_nr")
... )
shape: (3, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ u32 ┆ u32 │
╞═════╪═════╡
│ 0 ┆ 0 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 1 ┆ 1 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 2 ┆ 0 │
└─────┴─────┘
polars.LazyFrame.var will return variance value for each column in a table as below:
>>> df = pl.DataFrame({"a": [1, 2, 3, 4], "b": [1, 2, 1, 1], "c": [1, 1, 1, 1]}).lazy()
>>> df.collect()
shape: (4, 3)
┌─────┬─────┬─────┐
│ a ┆ b ┆ c │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ 1 ┆ 1 ┆ 1 │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ 2 ┆ 2 ┆ 1 │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ 3 ┆ 1 ┆ 1 │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ 4 ┆ 1 ┆ 1 │
└─────┴─────┴─────┘
>>> df.var().collect()
shape: (1, 3)
┌──────────┬──────┬─────┐
│ a ┆ b ┆ c │
│ --- ┆ --- ┆ --- │
│ f64 ┆ f64 ┆ f64 │
╞══════════╪══════╪═════╡
│ 1.666667 ┆ 0.25 ┆ 0.0 │
└──────────┴──────┴─────┘
I wish to select columns with value > 0 from LazyFrame but couldn't find the solution.
I can iterate over columns in polars dataframe then filter columns by condition as below:
>>> data.var()
shape: (1, 3)
┌──────────┬──────┬─────┐
│ a ┆ b ┆ c │
│ --- ┆ --- ┆ --- │
│ f64 ┆ f64 ┆ f64 │
╞══════════╪══════╪═════╡
│ 1.666667 ┆ 0.25 ┆ 0.0 │
└──────────┴──────┴─────┘
>>> cols = pl.select([s for s in data.var() if (s > 0).all()]).columns
>>> cols
['a', 'b']
>>> data.select(cols)
shape: (4, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1 ┆ 1 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 2 ┆ 2 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 3 ┆ 1 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 4 ┆ 1 │
└─────┴─────┘
But it doesn't work in LazyFrame:
>>> data = data.lazy()
>>> data
<polars.internals.lazyframe.frame.LazyFrame object at 0x7f0e3d9966a0>
>>> cols = pl.select([s for s in data.var() if (s > 0).all()]).columns
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 1, in <listcomp>
File "/home/jasmine/miniconda3/envs/jupyternb/lib/python3.9/site-packages/polars/internals/lazyframe/frame.py", line 421, in __getitem__
raise TypeError(
TypeError: 'LazyFrame' object is not subscriptable (aside from slicing). Use 'select()' or 'filter()' instead.
The reason for doing this in LazyFrame is that we want to maximize the performance. Any advice would be much appreciated. Thanks!
polars doesn't know what the variance is until after it is calculated but that's the same time that it is displaying the results so there's no way to filter the columns reported and also have it be more performant than just displaying all the columns, at least with respect to the polars calculation. It could be that python/jupyter takes longer to display more results than fewer.
With that said you could do something like this:
df.var().melt().filter(pl.col('value')>0).collect()
which gives you what you want in one line but it's a different shape.
You could also do something like this:
dfvar=df.var()
dfvar.select(dfvar.melt().filter(pl.col('value')>0).select('variable').collect().to_series().to_list()).collect()
Building on the answer from #dean MacGregor, we:
do the var calculation
melt
apply the filter
extract the variable column with column names
pass it as a list to select
df.select(
(
df.var().melt().filter(pl.col('value')>0).collect()
["variable"]
)
.to_list()
).collect()
shape: (4, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1 ┆ 1 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 2 ┆ 2 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 3 ┆ 1 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 4 ┆ 1 │
└─────┴─────┘
```
Any way to make the dynamic polars expressions not break with errors?
Currently I'm just excluding the columns by type, but just wondering if there is a better way.
For example, i have a df coming from parquet, if i just execute an expression on all columns it might break for certain types. Instead I want to contain these errors and possibly return a default value like None or -1 or something else.
import polars as pl
df = pl.scan_parquet("/path/to/data/*.parquet")
print(df.schema)
# Prints: {'date_time': <class 'polars.datatypes.Datetime'>, 'incident': <class 'polars.datatypes.Utf8'>, 'address': <class 'polars.datatypes.Utf8'>, 'city': <class 'polars.datatypes.Utf8'>, 'zipcode': <class 'polars.datatypes.Int32'>}
Now if i form generic expression on top of this, there are chances it may fail. For example,
# Finding positive count across all columns
# Fails due to: exceptions.ComputeError: cannot compare Utf8 with numeric data
print(df.select((pl.all() > 0).count().prefix("__positive_count_")).collect())
# Finding positive count across all columns
# Fails due to: pyo3_runtime.PanicException: 'unique_counts' not implemented for datetime[ns] data types
print(df.select(pl.all().unique_counts().prefix("__unique_count_")).collect())
# Finding positive count across all columns
# Fails due to: exceptions.SchemaError: Series dtype Int32 != utf8
# Note: this could have been avoided by doing an explict cast to string first
print(df.select((pl.all().str.lengths() > 0).count().prefix("__empty_count_")).collect())
I'll keep to things that work in lazy mode, as it appears that you are working in lazy mode with Parquet files.
Let's use this data as an example:
import polars as pl
from datetime import datetime
df = pl.DataFrame(
{
"col_int": [-2, -2, 0, 2, 2],
"col_float": [-20.0, -10, 10, 20, 20],
"col_date": pl.date_range(datetime(2020, 1, 1), datetime(2020, 5, 1), "1mo"),
"col_str": ["str1", "str2", "", None, "str5"],
"col_bool": [True, False, False, True, False],
}
).lazy()
df.collect()
shape: (5, 5)
┌─────────┬───────────┬─────────────────────┬─────────┬──────────┐
│ col_int ┆ col_float ┆ col_date ┆ col_str ┆ col_bool │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ f64 ┆ datetime[ns] ┆ str ┆ bool │
╞═════════╪═══════════╪═════════════════════╪═════════╪══════════╡
│ -2 ┆ -20.0 ┆ 2020-01-01 00:00:00 ┆ str1 ┆ true │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ -2 ┆ -10.0 ┆ 2020-02-01 00:00:00 ┆ str2 ┆ false │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 0 ┆ 10.0 ┆ 2020-03-01 00:00:00 ┆ ┆ false │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 2 ┆ 20.0 ┆ 2020-04-01 00:00:00 ┆ null ┆ true │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 2 ┆ 20.0 ┆ 2020-05-01 00:00:00 ┆ str5 ┆ false │
└─────────┴───────────┴─────────────────────┴─────────┴──────────┘
Using the col Expression
One feature of the col expression is that you can supply a datatype, or even a list of datatypes. For example, if we want to contain our queries to floats, we can do the following:
df.select((pl.col(pl.Float64) > 0).sum().suffix("__positive_count_")).collect()
shape: (1, 1)
┌────────────────────────────┐
│ col_float__positive_count_ │
│ --- │
│ u32 │
╞════════════════════════════╡
│ 3 │
└────────────────────────────┘
(Note: (pl.col(...) > 0) creates a series of boolean values that need to be summed, not counted)
To include more than one datatype, you can supply a list of datatypes to col.
df.select(
(pl.col([pl.Int64, pl.Float64]) > 0).sum().suffix("__positive_count_")
).collect()
shape: (1, 2)
┌──────────────────────────┬────────────────────────────┐
│ col_int__positive_count_ ┆ col_float__positive_count_ │
│ --- ┆ --- │
│ u32 ┆ u32 │
╞══════════════════════════╪════════════════════════════╡
│ 2 ┆ 3 │
└──────────────────────────┴────────────────────────────┘
You can also combine these into the same select statement if you'd like.
df.select(
[
(pl.col(pl.Utf8).str.lengths() == 0).sum().suffix("__empty_count"),
pl.col(pl.Utf8).is_null().sum().suffix("__null_count"),
(pl.col([pl.Float64, pl.Int64]) > 0).sum().suffix("_positive_count"),
]
).collect()
shape: (1, 4)
┌──────────────────────┬─────────────────────┬──────────────────────────┬────────────────────────┐
│ col_str__empty_count ┆ col_str__null_count ┆ col_float_positive_count ┆ col_int_positive_count │
│ --- ┆ --- ┆ --- ┆ --- │
│ u32 ┆ u32 ┆ u32 ┆ u32 │
╞══════════════════════╪═════════════════════╪══════════════════════════╪════════════════════════╡
│ 1 ┆ 1 ┆ 3 ┆ 2 │
└──────────────────────┴─────────────────────┴──────────────────────────┴────────────────────────┘
The Cookbook has a handy list of datatypes.
Using the exclude expression
Another handy trick is to use the exclude expression. With this, we can select all columns except columns of certain datatypes. For example:
df.select(
[
pl.exclude(pl.Utf8).max().suffix("_max"),
pl.exclude([pl.Utf8, pl.Boolean]).min().suffix("_min"),
]
).collect()
shape: (1, 7)
┌─────────────┬───────────────┬─────────────────────┬──────────────┬─────────────┬───────────────┬─────────────────────┐
│ col_int_max ┆ col_float_max ┆ col_date_max ┆ col_bool_max ┆ col_int_min ┆ col_float_min ┆ col_date_min │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ f64 ┆ datetime[ns] ┆ u32 ┆ i64 ┆ f64 ┆ datetime[ns] │
╞═════════════╪═══════════════╪═════════════════════╪══════════════╪═════════════╪═══════════════╪═════════════════════╡
│ 2 ┆ 20.0 ┆ 2020-05-01 00:00:00 ┆ 1 ┆ -2 ┆ -20.0 ┆ 2020-01-01 00:00:00 │
└─────────────┴───────────────┴─────────────────────┴──────────────┴─────────────┴───────────────┴─────────────────────┘
Unique counts
One caution: unique_counts results in Series of varying lengths.
df.select(pl.col("col_int").unique_counts().prefix(
"__unique_count_")).collect()
shape: (3, 1)
┌────────────────────────┐
│ __unique_count_col_int │
│ --- │
│ u32 │
╞════════════════════════╡
│ 2 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2 │
└────────────────────────┘
df.select(pl.col("col_float").unique_counts().prefix(
"__unique_count_")).collect()
shape: (4, 1)
┌──────────────────────────┐
│ __unique_count_col_float │
│ --- │
│ u32 │
╞══════════════════════════╡
│ 1 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2 │
└──────────────────────────┘
As such, these should not be combined into the same results. Each column/Series of a DataFrame must have the same length.
https://pola-rs.github.io/polars/py-polars/html/reference/api/polars.DataFrame.join.html
Can I specify the default NULL value for outer joins? Like 0?
The join method does not currently have an option for setting a default value for nulls. However, there is an easy way to accomplish this.
Let's say we have this data:
import polars as pl
df1 = pl.DataFrame({"key": ["a", "b", "d"], "var1": [1, 1, 1]})
df2 = pl.DataFrame({"key": ["a", "b", "c"], "var2": [2, 2, 2]})
df1.join(df2, on="key", how="outer")
shape: (4, 3)
┌─────┬──────┬──────┐
│ key ┆ var1 ┆ var2 │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪══════╪══════╡
│ a ┆ 1 ┆ 2 │
├╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ b ┆ 1 ┆ 2 │
├╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ c ┆ null ┆ 2 │
├╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ d ┆ 1 ┆ null │
└─────┴──────┴──────┘
To create a different value for the null values, simply use this:
df1.join(df2, on="key", how="outer").with_column(pl.all().fill_null(0))
shape: (4, 3)
┌─────┬──────┬──────┐
│ key ┆ var1 ┆ var2 │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪══════╪══════╡
│ a ┆ 1 ┆ 2 │
├╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ b ┆ 1 ┆ 2 │
├╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ c ┆ 0 ┆ 2 │
├╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ d ┆ 1 ┆ 0 │
└─────┴──────┴──────┘
I cannot find how to reorder columns in a polars dataframe in the polars DataFrame docs.
thx
Using the select method is the recommended way to sort columns in polars.
Example:
Input:
df
┌─────┬───────┬─────┐
│Col1 ┆ Col2 ┆Col3 │
│ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str │
╞═════╪═══════╪═════╡
│ a ┆ x ┆ p │
├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┤
│ b ┆ y ┆ q │
└─────┴───────┴─────┘
Output:
df.select(['Col3', 'Col2', 'Col1'])
or
df.select([pl.col('Col3'), pl.col('Col2'), pl.col('Col1)])
┌─────┬───────┬─────┐
│Col3 ┆ Col2 ┆Col1 │
│ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str │
╞═════╪═══════╪═════╡
│ p ┆ x ┆ a │
├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┤
│ q ┆ y ┆ b │
└─────┴───────┴─────┘
Note:
While df[['Col3', 'Col2', 'Col1']] gives the same result (version 0.14), it is recommended (link) that you use the select method instead.
We strongly recommend selecting data with expressions for almost all
use cases. Square bracket indexing is perhaps useful when doing
exploratory data analysis in a terminal or notebook when you just want
a quick look at a subset of data.
For all other use cases we recommend using expressions because:
expressions can be parallelized
the expression approach can be used in lazy and eager mode while the indexing approach can only be used in eager mode
in lazy mode the query optimizer can optimize expressions
Turns out it is the same as pandas:
df = df[['PRODUCT', 'PROGRAM', 'MFG_AREA', 'VERSION', 'RELEASE_DATE', 'FLOW_SUMMARY', 'TESTSUITE', 'MODULE', 'BASECLASS', 'SUBCLASS', 'Empty', 'Color', 'BINNING', 'BYPASS', 'Status', 'Legend']]
That seems like a special case of projection to me.
df = pl.DataFrame({
"c": [1, 2],
"a": ["a", "b"],
"b": [True, False]
})
df.select(sorted(df.columns))
shape: (2, 3)
┌─────┬───────┬─────┐
│ a ┆ b ┆ c │
│ --- ┆ --- ┆ --- │
│ str ┆ bool ┆ i64 │
╞═════╪═══════╪═════╡
│ a ┆ true ┆ 1 │
├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┤
│ b ┆ false ┆ 2 │
└─────┴───────┴─────┘